Parasympathetic Physiology and Anticholinergic Pharmacology

Anticholinergic drugs and the many, many more drugs with anticholinergic side effects are ubiquitous in medical practice. The presentation below will describe parasympathetic physiology as well as anticholinergic pharmacology.

Use the pause and play buttons to move through the flash animations below or download the powerpoint for free at the bottom of this page.

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Dopamine and Acetylcholine in Schizophrenia, Delirium and Parkinson’s

Dopamine and Acetylcholine are key players in Psychiatric and Extrapyramidal Physiology and Pharmacology. The presentation below will help to clarify these affects and also help you to remember them.


Psychiatric neurotransitters are still a matter of theory and speculation. The actual mechanisms presented may , in the future, turn out to be completely bogus. For now they will help you in your clinical approach to patients.

Use the pause and play buttons to move through the flash animations below or download the powerpoint for free at the bottom of this page.

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Clonidine and Beta Blocker Interaction

The interaction between clonidine and beta blockers (metoprolol, atenolol, carvedilol, nadolol, etc.) is often foggy in the mind of health care professionals. This confusion stems from the fact that the interaction is only relevant when the patient misses a dose or two and you have clonidine withdrawl.

Use the pause and play buttons to move through the flash animations below or scroll down to download the powerpoint for free.

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Beta Blocker Pharmacology

Beta blockers are some of the most commonly used drugs. Understanding the pharmacology is foundational to good pharmacy and medical practice. You might want to start with the One Minute Genius on Sympathetic Drive first since this one is based on an understanding of Sympathetic Physiology. When you are ready, click on the beta blocker graphic.

If you can’t see the flash video in a few seconds then download the Beta Blocker Pharmacology Power Point

Use the play button to move through the powerpoint animations below.

Beta Blocker Pharmacology
Beta Blocker PDF

Sympathetic Nervous System Physiology

Understanding the sympathetic nervous system is critical to understanding the pharmacology of a myriad of drugs. This power point will refresh your feeble memories and make it all clear. The key is to understand all the effects as a system designed for survival.

Use the pause and play buttons to move through the flash animations below or download the powerpoint for free –> [wpfilebase tag=”file” id=4 /]

Estimating Renal Function

For Research Purposes

The Gold Standard for GFR is iodine 125 labeled iothalamate clearance

equation for calculating actual creatinine clearance
The Gold Standard for Creatinine Clearance is :

For Clinical Purposes

Evaluating and Monitoring Renal Function

  • The state of the art equation for estimating GFR (eGFR) is the CKD-EPI equation.
  • This equation is an update to the MDRD equation that fixes the overestimate at higher GFRs
  • The CKD-EPI gives an estimated GFR (eGFR) normalized to 1.73 m2


  • GFR vs Creatinine Clearance
  • For an assessment of renal function, you want to estimate GFR
  • Creatinine clearance approximates GFR, but it is not exact because creatinine is secreted in the renal tubule as well as filtered at the glomerulus. Therefore creatinine clearance overestimates GFR. The over estimate due to secretion of creatinine becomes more significant as GFR decreases.

Adjusting Drug Doses

For the reasons given above, the CKDepi should be the best equation for adjusting dosages for renally cleared drugs.   HOWEVER…..

cockcroft and gault equation

So this is probably what you should still use.   Yes, with all it’s shortcomings.

Frequently Asked Questions:

What about correcting Cockcroft and Gault (normalizing it to a 72kg person)?

Don’t bother. If you want a normalized measure, use the CKD-EPI

What about rounding up the creatinine to 0.8?

  • For the CKD-EPI you don’t need to even think about it, they have included modified calculations for low SCr.
  • For the Cockroft and Gault:
    1. Rounding to 0.8 probably makes sense IF it is a frail person that probably has less lean mass and therefore produces less creatinine
    2. Data to support this was derived prior to standardization of laboratory creatinine values.
    3. Manufacturers never report doing this when developing dosage adjustment recommendations.
    4. Generally…. don’t do it.

So what is the most accurate estimate of Creatinine Clearance?

For people with Creatinine clearances greater than 30ml/min , Cockcroft and Gault gives the best estimate of actual creatine clearance. But often that is not what you want to know.

The problem is that drug clearance correlates better with GFR than with Creatinine Clearance. Creatinine clearance is generally higher than GFR because creatinine is secreted by the renal tubule in ADDITION to being filtered.

Which weight should I use for Cockcroft and Gault?

  • The data comes from non-obese healthy people
  • Creatinine comes from lean mass
  • Therefore a reasonable approach would be to use Ideal body weight (IBW) plus 40% of weight in excess of IBW. Use weight= IBW + 0.40(Total Body Weight – IBW)
  • IBW= 50kg plus inches over 5 feet for men and 45kg plus inches over 5 ft for men.

Bottom LIne: How do we adjust drug doses for renal impairment?

  1. Check the package insert for the method that was used in developing the dosage adjustment recommendations.
  2. If no method is stated, use the original C&G equation.
  3. If the patient has a low serum creatinine use the CKD-EPI equation and denormalize it. (multiply the creatinine clearance calculated by the patients body surface area / 1.73 m2

Sensitivity and Specificity

Click on the arrows to page through the tutorial on the statistical concepts of specificity and sensitivity. In my experience, people get confused when they try to learn it first as a formula and not as a concept. The words sensitive and specific are not difficult, so if you start from there, you will find it is much easier to learn. This tutorial is the joint effort of Profjameson and two Pharm. D. candidates, Caleb Bryant and Nicholas Anderson.

Ideally all tests would be very sensitive and very specific.  Unfortunately, that is rarely the case.   Sensitivity and Specificity are more or less important depending on the purpose of the test.  A broad screening test needs to be fairly sensitive to be of any value.   On the other hand a test must be specific to be of value for a definitive diagnosis.  That is why the more sensitive (a less specific) ELISA test is used to screen for HIV, but a Western Blot (very specific) test is done to confirm it.

Interestingly, the rapid strep screen is only about 75% sensitive  (misses 25% of people who truly have strep), but is fairly specific (very few false positives).

Type One and Type Two Error

The purpose of inferential statistics is to predict differences between groups in the general population by measuring the difference in a small sample.


  • Blood pressure lowering of two drugs: Whocaresapine vs Lowpressure
  • The rate of venous thrombosis in knee replacements prophylaxed with Digabigatran vs Goldiloxaparin

As you know, a p value is the probability that the observed difference is due to random chance. However there are two main types of errors you can make.


Type One Error in statistics
You detected a difference in the sample when there truly is no difference in the larger population (oops)

  • This is like a false positive (also known as an alpha error)
  • Associated with alpha / p-value
    • Alpha is the highest ACCEPTABLE probability that the measured outcome was due random chance
        Standard value for alpha is 0.05, or 0.025 for a two-tailed test
  • the p value is the MEASURED probability that your outcome is due to random chance
  • If your p-value (measured) is less than alpha (highest acceptable) then the difference is considered to be unlikely to have occurred due to chance.
  • A type I error can only occur when your p value is less than alpha. However, as your p-value increases towards alpha it is more likely that you are committing a type I error.
  • The probability of random chance producing a difference is additive with multiple comparisons. The more things you compare, the more likely you are to commit a type I error.

Let’s do a fun example:

  • A new drug (LowStress is coming to market. During the testing period, LowStress was shown to make people happier than placebo. On placebo 15 % of people were happy. On LowStress, 22% of people were happy. The alpha was set at 0.05, and the p-value that LowStress made more people happy versus placebo was 0.04. This indicates that there is a 4% chance that more people were happier due to random events not related to LowStress.
  • An alpha value of 0.05 means there is a 5% probability that more people would be happy due to random chance and not LowStress, which is the standard acceptable value. Because the p-value of 0.04 is less than alpha we are believe that the results seen with LowStress were not due to random chance.
  • Notice : There is still a 4% chance (1 out of 25) that this difference was, in fact, due to random chance. If it is due to random chance , we have committed a type I error.

Type Two Error
You fail to detect a difference in the Sample when there truly is a difference in the Population

AKA false negative or a beta error
  • Beta is directly related to power (1-beta = power).
    • Acceptable standard for power is 80% (see 1 minute genus on power), therefore the acceptable standard for beta is 20%.
    • This means that there is a 20% chance that you will detect a difference when there is no difference or you are 80% confident that you would have detected a difference in the sample if it exists in the population
  • As power increases your risk of committing a type II error decreases
  • If your p value is statistically significant <0.05) then you had enough power !!! Even if the number in the study was less than originally ESTIMATED. It was only an estimation. IF your p value is statistically insignificant (>0.05) THEN

    Eitherthere is really no difference OR you committed a type II error.

    Let’s revisit our fun example:

    You had drastic cuts made to your research budget and could only enroll 30 people in each group (LowStress vs Placebo). You found that 15% of people on placebo were happy and 22% of people on Lowstress were happy. But the p value is 0.34. You found no difference. However, you have probably committed a Type II error due to the small study size (inadequate power)

    Insulin Dosing Rules

    John and Albert in Bali Albert and I had to go into the beautiful mountains of the island of Bali to research these rules. Bali is one of many islands that make up the country of Indonesia. Somebody had to go.

    Insulin Dosing Rules

    Starting dose

    0.5 to 1 unit / kg / day for Type 2 Diabetes
    If they are already on Insulin Start with their current dose
    Give 50% long acting and 50% short acting. Divide the short acting evenly for each meal.

    Corrective Insulin (in addtion to meal insulin)

    Estimated Blood sugar Decrease for Each Unit of Insulin

    1700/ total daily insulin for Humalog and Novolog
    1500/ total daily insulin for regular insulin

    Insulin for Carb Counters

    Number of grams of carbohydrate covered by each unit of insulin

    450/ total daily insulin for Humalog and Novolog
    500/ total daily insulin for regular insulin

    Power Calculations

    Statistical Power

    OK, so John Lennon didn’t really write this , but statistical power is a very abstract concept and the ability to “imagine” really helps.

    Power is the probability that you will find a statistically significant difference in your study SAMPLE if it truly exists in the larger POPULATION.

    Beta is the probability that you will not be able to detect a difference if it is truly there in the population.

    Hypothetical Dilemma

    The study you can’t afford: There are 72,000,000 people with hypertension in the U.S. If you could study them all you would find that the new drug Lowpressure® lowers blood pressure by 7mm more than Whocaresapine.

    To test the difference on an affordable scale, you need:

    Power Calculations Before the Study !!

    We will describe the process in four simple steps.

    power before the study one


    Decide how big a difference you consider clinically important.
    For our Hypothetical Dilemma Example: You think a 7mm difference or more is clinically important.


    power before the study one


    How variable is the outcome we are testing? (this is a guess, based on available facts)
    Fact: The measure of variability used in power calculations is variance or (standard deviation)2

    Hypothetical Dilemma: From previous studies, we know that standard deviation of the mean blood pressure has been 5mmg Hg ( so variance for the calculation would be 25 (5)2)

    Fact: The more variable the outcome, the more difficult it is to be statistically confident that the difference you observe is real and not due to random chance (or variation)

    Fact: the more variable the data , the more people you have to study to get statistical significance.

    power before the study three


    How sure do we need to be?

    The usual beta is 0.20 (giving a power of 80%)
    If you haven’t picked this up yet, One minus beta = Power.

    power before the study four


    The Dreaded Calculation

    Because the concept is the important thing, we will spare you the headache of the power equation and just tell you that these assumptions yield a calculated N required of approximately 100 in each group.

    Hypothetical Dilemma Example: You will need to study 200 people, randomized to the drug “Lowpressure” or the drug “Whocaresapine” to have an 80 percent power to detect a 7mm difference or more.


    However, studies often don’t enroll exactly the number of people they need so you may have to do ….

    Power Calculations After the Study !!

    Don’t despair, there are only three steps for this part.
    power after the study one


    If you found a statistically significant difference (p less than 0.05)…You had enough POWER. You don’t need power calculations. Really.


    power after the study one


    If you found a statistically non-significant difference (p greater than 0.05). There are two main possibilities.

    A. There really is no difference in the population

    B. You didn’t have enough power. (Congratulations! You have succeeded in making a Type II error

    power afterthe study three


    Since you are the insatiably curious type, we can now calculate The Power we had to detect the difference we said was significant.

    We will use:

    • N: the number of people you actually enrolled
    • Sigma: the measured variance of the blood pressures in your study population (not estimated as before)
    • The difference you decided before the study was clinically important (7 mm Hg in this case)
    • The power you calculate from this is the probability you had of detecting a clinically important difference if it is present in the larger population.